Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2401.13298.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:50:44.212283Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T23:48:38.967134Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 94bc66a4-5db6-4e77-92fc-ecca28ad514f · inbound
Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities cites this paper.
Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Reference 79
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:48:38.972145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 951730d5-67a3-458f-899c-6651b34cefbb · inbound
ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data? cites this paper.
ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data? Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:50:44.212283Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29ff0f57-d177-4769-a48e-9a9726e020a8 · inbound
Explainability for Vision Foundation Models: A Survey cites this paper.
Explainability for Vision Foundation Models: A Survey Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Reference 107
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:26:35.370720Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.